Repeated Measures ANOVA (Non-parametric)
The Friedman test is used to explore the relationship between a continuous dependent variable and a categorical explanatory variable, where the explanatory variable is ‘within subjects’ (where multiple measurements are from the same subject). It is analagous to Repeated Measures ANOVA, but with the advantage of being non-parametric, and not requiring the assumptions of normality or homogeneity of variances. However, it has the limitation that it can only test a single explanatory variable at a time.
Example usage
data('bugs', package = 'jmv')
anovaRMNP(bugs, measures = vars(LDLF, LDHF, HDLF, HDHF))
#
# REPEATED MEASURES ANOVA (NON-PARAMETRIC)
#
# Friedman
# ────────────────────────
# χ² df p
# ────────────────────────
# 55.8 3 < .001
# ────────────────────────
#
Arguments
| data | the data as a data frame |
| measures | a vector of strings naming the repeated measures variables |
| pairs | TRUE or FALSE (default), perform pairwise comparisons |
| desc | TRUE or FALSE (default), provide descriptive statistics |
| plots | TRUE or FALSE (default), provide a descriptive plot |
| plotType | 'means' (default) or 'medians', the error bars to use in the plot |
Returns
A results object containing:
| results$table | a table |
| results$comp | a table |
| results$desc | a table |
| results$plot | an image |
Tables can be converted to data frames with asDF or as.data.frame(). For example:
results$table$asDF
as.data.frame(results$table)